ArticleBiosensors2022
Utilization of Personalized Machine-Learning to Screen for Dysglycemia from Ambulatory ECG, toward Noninvasive Blood Glucose Monitoring.
Article in Biosensors, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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The trial behind it
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Who cites it
10 citing papers in PubMed.
- Advances in Electrocardiogram-Based Non-Invasive Blood Glucose Monitoring Technology.Diabetes, obesity & metabolism · 2026Review
- An improved ICEEMDAN-depth hybrid network model integrating multimodal data for the screening of diabetic peripheral neuropathy.Scientific reports · 2026Article
- Development and Pilot Evaluation of a Wearable 12-Lead ECG System for Multilead Feature Analysis in Individuals with Different Glycemic Status.Sensors (Basel, Switzerland) · 2026Article
- Factors associated with physician modifications to automated ECG interpretations.European heart journal. Digital health · 2026Article
- Artificial intelligence in prediabetes care: applications in screening, risk prediction, and lifestyle intervention.Frontiers in endocrinology · 2026Review
- Abnormality prediction and forecasting of laboratory values from electrocardiogram signals using multimodal deep learning.Scientific reports · 2025Article
- Advancements of Glucose Monitoring Biosensor: Current State, Generations of Technological Progress and Innovation Dynamics.Current pharmaceutical biotechnology · 2025Review
- Serum Potassium Monitoring Using AI-Enabled Smartwatch Electrocardiograms.JACC. Clinical electrophysiology · 2024Article
- Predicting Dysglycemia in Patients with Diabetes Using Electrocardiogram.Diagnostics (Basel, Switzerland) · 2024Article
- Advancements in Artificial Intelligence in Emergency Medicine in Taiwan: A Narrative Review.Journal of acute medicine · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Blood glucose (BG) monitoring is important for critically ill patients, as poor sugar control has been associated with increased mortality in hospitalized patients. However, constant BG monitoring can be resource-intensive and pose a healthcare burden in clinical practice. In this study, we aimed to develop a personalized machine-learning model to predict dysglycemia from electrocardiogram (ECG) data. We used the Medical Information Mart for Intensive Care III database as our source of data and obtained more than 20 ECG records from each included patient during a single hospital admission. We focused on lead II recordings, along with corresponding blood sugar data. We processed the data and used ECG features from each heartbeat as inputs to develop a one-class support vector machine algorithm to predict dysglycemia. The model was able to predict dysglycemia using a single heartbeat with an AUC of 0.92 ± 0.09, a sensitivity of 0.92 ± 0.10, and specificity of 0.84 ± 0.04. After applying 10 s majority voting, the AUC of the model's dysglycemia prediction increased to 0.97 ± 0.06. This study showed that a personalized machine-learning algorithm can accurately detect dysglycemia from a single-lead ECG.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.